llamaindex

from llama_index.core import SimpleDirectoryReader, Document
from llama_index.readers.web import SimpleWebPageReader
from llama_index.readers.github import GithubRepositoryReader
 
# Directory of files
documents = SimpleDirectoryReader("./data").load_data()
 
# Web pages
reader = SimpleWebPageReader()
documents = reader.load_data(["https://example.com"])
 
# GitHub repository
reader = GithubRepositoryReader(owner="user", repo="repo")
documents = reader.load_data(branch="main")
 
# Manual document creation
doc = Document(
    text="This is the document content",
    metadata={"source": "manual", "date": "2025-01-01"}
)

from llama_index.core import VectorStoreIndex, ListIndex, TreeIndex
 
# Vector index (most common - semantic search)
vector_index = VectorStoreIndex.from_documents(documents)
 
# List index (sequential scan)
list_index = ListIndex.from_documents(documents)
 
# Tree index (hierarchical summary)
tree_index = TreeIndex.from_documents(documents)
 
# Save index
index.storage_context.persist(persist_dir="./storage")
 
# Load index
from llama_index.core import load_index_from_storage, StorageContext
storage_context = StorageContext.from_defaults(persist_dir="./storage")
index = load_index_from_storage(storage_context)

# Basic query
query_engine = index.as_query_engine()
response = query_engine.query("What is the main topic?")
print(response)
 
# Streaming response
query_engine = index.as_query_engine(streaming=True)
response = query_engine.query("Explain quantum computing")
for text in response.response_gen:
    print(text, end="", flush=True)
 
# Custom configuration
query_engine = index.as_query_engine(
    similarity_top_k=3,          # Return top 3 chunks
    response_mode="compact",     # Or "tree_summarize", "simple_summarize"
    verbose=True
)

# Vector retriever
retriever = index.as_retriever(similarity_top_k=5)
nodes = retriever.retrieve("machine learning")
 
# With filtering
retriever = index.as_retriever(
    similarity_top_k=3,
    filters={"metadata.category": "tutorial"}
)
 
# Custom retriever
from llama_index.core.retrievers import BaseRetriever
 
class CustomRetriever(BaseRetriever):
    def _retrieve(self, query_bundle):
        # Your custom retrieval logic
        return nodes

from llama_index.core.agent import FunctionAgent
from llama_index.llms.openai import OpenAI
 
# Define tools
def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b
 
def add(a: int, b: int) -> int:
    """Add two numbers."""
    return a + b
 
# Create agent
llm = OpenAI(model="gpt-4o")
agent = FunctionAgent.from_tools(
    tools=[multiply, add],
    llm=llm,
    verbose=True
)
 
# Use agent
response = agent.chat("What is 25 * 17 + 142?")
print(response)

from llama_index.core.tools import QueryEngineTool
 
# Create index as before
index = VectorStoreIndex.from_documents(documents)
 
# Wrap query engine as tool
query_tool = QueryEngineTool.from_defaults(
    query_engine=index.as_query_engine(),
    name="python_docs",
    description="Useful for answering questions about Python programming"
)
 
# Agent with document search + calculator
agent = FunctionAgent.from_tools(
    tools=[query_tool, multiply, add],
    llm=llm
)
 
# Agent decides when to search docs vs calculate
response = agent.chat("According to the docs, what is Python used for?")

from llama_index.core.chat_engine import CondensePlusContextChatEngine
 
# Chat with memory
chat_engine = index.as_chat_engine(
    chat_mode="condense_plus_context",  # Or "context", "react"
    verbose=True
)
 
# Multi-turn conversation
response1 = chat_engine.chat("What is Python?")
response2 = chat_engine.chat("Can you give examples?")  # Remembers context
response3 = chat_engine.chat("What about web frameworks?")

from llama_index.core.vector_stores import MetadataFilters, ExactMatchFilter
 
# Filter by metadata
filters = MetadataFilters(
    filters=[
        ExactMatchFilter(key="category", value="tutorial"),
        ExactMatchFilter(key="difficulty", value="beginner")
    ]
)
 
retriever = index.as_retriever(
    similarity_top_k=3,
    filters=filters
)
 
query_engine = index.as_query_engine(filters=filters)

from pydantic import BaseModel
from llama_index.core.output_parsers import PydanticOutputParser
 
class Summary(BaseModel):
    title: str
    main_points: list[str]
    conclusion: str
 
# Get structured response
output_parser = PydanticOutputParser(output_cls=Summary)
query_engine = index.as_query_engine(output_parser=output_parser)
 
response = query_engine.query("Summarize the document")
summary = response  # Pydantic model
print(summary.title, summary.main_points)

# Load all supported formats
documents = SimpleDirectoryReader(
    "./data",
    recursive=True,
    required_exts=[".pdf", ".docx", ".txt", ".md"]
).load_data()

from llama_index.readers.web import BeautifulSoupWebReader
 
reader = BeautifulSoupWebReader()
documents = reader.load_data(urls=[
    "https://docs.python.org/3/tutorial/",
    "https://docs.python.org/3/library/"
])

from llama_index.readers.database import DatabaseReader
 
reader = DatabaseReader(
    sql_database_uri="postgresql://user:pass@localhost/db"
)
documents = reader.load_data(query="SELECT * FROM articles")

from llama_index.readers.json import JSONReader
 
reader = JSONReader()
documents = reader.load_data("https://api.example.com/data.json")

from llama_index.vector_stores.chroma import ChromaVectorStore
import chromadb
 
# Initialize Chroma
db = chromadb.PersistentClient(path="./chroma_db")
collection = db.get_or_create_collection("my_collection")
 
# Create vector store
vector_store = ChromaVectorStore(chroma_collection=collection)
 
# Use in index
from llama_index.core import StorageContext
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

from llama_index.vector_stores.pinecone import PineconeVectorStore
import pinecone
 
# Initialize Pinecone
pinecone.init(api_key="your-key", environment="us-west1-gcp")
pinecone_index = pinecone.Index("my-index")
 
# Create vector store
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
 
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

from llama_index.vector_stores.faiss import FaissVectorStore
import faiss
 
# Create FAISS index
d = 1536  # Dimension of embeddings
faiss_index = faiss.IndexFlatL2(d)
 
vector_store = FaissVectorStore(faiss_index=faiss_index)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
 
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

from llama_index.llms.anthropic import Anthropic
from llama_index.core import Settings
 
# Set global LLM
Settings.llm = Anthropic(model="claude-sonnet-4-5-20250929")
 
# Now all queries use Anthropic
query_engine = index.as_query_engine()

from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 
# Use HuggingFace embeddings
Settings.embed_model = HuggingFaceEmbedding(
    model_name="sentence-transformers/all-mpnet-base-v2"
)
 
index = VectorStoreIndex.from_documents(documents)

from llama_index.core import PromptTemplate
 
qa_prompt = PromptTemplate(
    "Context: {context_str}\n"
    "Question: {query_str}\n"
    "Answer the question based only on the context. "
    "If the answer is not in the context, say 'I don't know'.\n"
    "Answer: "
)
 
query_engine = index.as_query_engine(text_qa_template=qa_prompt)

from llama_index.core import SimpleDirectoryReader
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
 
# Load images and documents
documents = SimpleDirectoryReader(
    "./data",
    required_exts=[".jpg", ".png", ".pdf"]
).load_data()
 
# Multi-modal index
index = VectorStoreIndex.from_documents(documents)
 
# Query with multi-modal LLM
multi_modal_llm = OpenAIMultiModal(model="gpt-4o")
query_engine = index.as_query_engine(llm=multi_modal_llm)
 
response = query_engine.query("What is in the diagram on page 3?")

from llama_index.core.evaluation import RelevancyEvaluator, FaithfulnessEvaluator
 
# Evaluate relevance
relevancy = RelevancyEvaluator()
result = relevancy.evaluate_response(
    query="What is Python?",
    response=response
)
print(f"Relevancy: {result.passing}")
 
# Evaluate faithfulness (no hallucination)
faithfulness = FaithfulnessEvaluator()
result = faithfulness.evaluate_response(
    query="What is Python?",
    response=response
)
print(f"Faithfulness: {result.passing}")

# Complete RAG pipeline
documents = SimpleDirectoryReader("docs").load_data()
index = VectorStoreIndex.from_documents(documents)
index.storage_context.persist(persist_dir="./storage")
 
# Query
query_engine = index.as_query_engine(
    similarity_top_k=3,
    response_mode="compact",
    verbose=True
)
response = query_engine.query("What is the main topic?")
print(response)
print(f"Sources: {[node.metadata['file_name'] for node in response.source_nodes]}")

# Conversational interface
chat_engine = index.as_chat_engine(
    chat_mode="condense_plus_context",
    verbose=True
)
 
# Multi-turn chat
while True:
    user_input = input("You: ")
    if user_input.lower() == "quit":
        break
    response = chat_engine.chat(user_input)
    print(f"Bot: {response}")